Today … looking at Carlos Perez’s Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence
The claim: “This book is an opinionated take on the developments of Deep Learning AI” – a reviewers offers that it’s probably good to start out exploring the Intuition Machine blog on Medium. Appears oriented to the ‘first-entry into AI ‘ folks who want to get a sense of what’s going on. Really Deep AI /Deep Learning / Deep whatever is another matter.
To place Artificial Intelligence in appropriate context is a complex and intricate challenge. Marty Ford, a master explainer presents interviews with some of the principal architects of AI. The Architects in this case are Yoshua Bengio, Stuart Russell, Geoffrey Hinton, Nick Bostrom, Yann LeCun, Fei-Fei Li, Demis Hassabis, Andrew Ng, Rana El Kaliouby, Ray Kurzweil, Daniela Rus, James Manyika, Gary Marcus, Barbara Grosz, Judea Pearl, Jeffrey Dean, Daphne Koller, David, Ferrucci, Rodney Brooks, Cynthia Breazeal, Joshua Tenenbaum, Oren Etzioni, and Bryan Johnson.
What a great list … Dan Ferrucci is of course known from his amazing work with IBM WATSON, and the first ever amazing win of a machine over the best of the best at Jeopardy! Dennis Hassabis , of Google/Alphabet’s Deep Mind, brought us AlphaGo, AlphaGoZero, and now AlphaGo that exceeds the best of the best in Chess, GO and Shogi (all with the same MCTS algorithm). NYU/FAIR/FaceBook’s Yan LeCun did some serious stuff with Mastering and claiming ‘the prize’ over ImageNet Challenge. Rodney Brooks with iRobot, … each one of the Architects is truly a master architect. We’ll explore their contributions and significance later … their thoughts are really worth checking out. I am looking at all kinds of things right now … and there’s just so much. Maybe I need a nice Intelligent Machine Assistant to help me pull this al together. 🙂
Ford, M. (2018), Architects of Intelligence,
“The occupational activities of children are learning, thinking, playing and the like. Yet we tell them nothing about those things.” per AI Pioneer Seymour Papert – In Pam McCorduck’s Machines who Think, (an outstanding book; Pam is a great author, turns out she’s the wife of Joe Traub who was Computer Science Dept Chair at Carnegie Mellon University & Columbia University … and had amazing insight into the real story 🙂 – not found elsewhere ) https://amzn.to/2FwGmIu
EXCELLENT EXCELLENT BOOK … It’s really packed with amazing insights and details hidden from the public view …
I didn’t realize Papert’s connection with Piaget and his deep understanding and interest in how children learn. Of course Papert and Minsky’s Perceptrons were widely known [ and got a refresh boost . The Perceptron. ideas… which, in prehistoric times, with Marvin Minsky, helped pave the way to the AI we know today. — that’s where the real action was and maybe still is … check the reboot. over at https://amzn.to/2TNjok7
One of the main categories of discussion in this book is that of worthwhile tasks for AI. I will devote some time to stating some of the recognized questions, problems, and tasks. I will also mention some notable AI accomplishments and highlight a few of the recognized scholarly achievements. Another topic for discussion is the classification of Intelligences. What is Natural Intelligence? What is Artificial General Intelligence? What is Superintelligence? What about human measures such as IQ? G? What does the AlphaZero algorithm beating the best human players in Chess, Go and Shogi mean? Can the Paperclip Apocalypse really happen?
All these and more … coming soon …
OK, so I started perusing Terry Sejnowski’s recent book, The Deep Learning Revolution. It’s dedicated to Bo and Sol, Theresa, and Joseph and is In memory of Solomon Golomb. Nice!
- It’s a great book. In the short time I spend with it, I learned quite a lot. I decided to see what’s most important to Terry looking at the topics he spends most of his time on. Here’s what pops out first …neural networks and deep learning . [To be expected], then the items getting most discussion are:
- the brain
- machine learning
- learning algorithm
- artificial intelligence
- the world
- visual cortex
- the network
- boltzmann machine
- the cortex
- Geoffrey Hinton [looks like Geoff is really getting attention and kudos from everyone!!]
- network models
- the future
- self driving car
- learning networks
- cost function
- deep learning networks
- hopfield net
- primary visual cortex
- the visual cortex
- independent component analysis
- real world
- the internet
- the perceptron
- facial expressions
- reinforcement learning
- Francis Crick
- hidden units
- the retina
- information processing systems
- neural information processing
- neural information processing systems
- td gammon
- the boltzmann machine
- computer vision
- driving cars
- simple cells
- the hopfield net
- cerebral cortex
- David Hubel
Somewhere further down the list I came across Soumith Chintala over at FaceBook AI / Courant Institute. His was a new name for me. Looks like he’s a PyTorch maven, super-coder. Nice! his Wasserstein Generative Adversarial Network (GAN) paper seems pretty nice. Apparently FAIR has advanced the ball a lot with Generative Adversarial Networks. I need to be paying much more attention. Also noted a new name to follow, Cade Metz who writes about technology for The New York Times/
All this from my first glance at The Deep Learning Revolution.
read it … I will get deeper into the deep learning as well.
Happy Holidays …
At Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., not too long ago: DOE announced “Aurora” supercomputer is on track to be the United States’ first exascale system. It will be built by Intel and Cray for Argonne National Laboratory, delivery date has shifted from 2018 to 2021 and target capability has been expanded from 180 petaflops to 1,000 petaflops (1 exaflop).
Wow! One can only speculate about what this means for Artificial and Advanced Intelligence (AI/AI) and the progression to the Singularity. ExaIntelligence Arriving.